The field of data science, and more particularly, the development and implementation of analytical models, has typically required strong computer and processing system skills and familiarity with data science. These specialized skills were needed to develop, setup, and program model algorithms and to access and prepare data so that the data was effective for training the model, and so that running the model on the data would give meaningful results. These complex technical challenges have traditionally left scientists and engineers with the daunting task of building and implementing analytical models that are useful in their engineering and scientific fields. That is, analytical modeling is typically a field in which scientists and engineers have less familiarity, and which in any event is tangential to their primary goal of extracting insights from data.
Additionally, creating accurate analytical models is often an experimental process requiring multiple iterative cycles of hypothesis testing. The iterative cycles can each take significant time to setup and/or complete (e.g., days or months). The extended creation, setup, and/or training process hinders development of additional analytical models and analytical models of higher accuracy.